Empowering parallel computing with field programmable gate arrays

After more than 30 years, reconfigurable computing has grown from a concept to a mature field of science and technology. The cornerstone of this evolution is the field programmable gate array, a building block enabling the configuration of a custom hardware architecture. The departure from static von Neumannlike architectures opens the way to eliminate the instruction overhead and to optimize the execution speed and power consumption. FPGAs now live in a growing ecosystem of development tools, enabling software programmers to map algorithms directly onto hardware. Applications abound in many directions, including data centers, IoT, AI, image processing and space exploration. The increasing success of FPGAs is largely due to an improved toolchain with solid high-level synthesis support as well as a better integration with processor and memory systems. On the other hand, long compile times and complex design exploration remain areas for improvement. In this paper we address the evolution of FPGAs towards advanced multi-functional accelerators, discuss different programming models and their HLS language implementations, as well as high-performance tuning of FPGAs integrated into a heterogeneous platform. We pinpoint fallacies and pitfalls, and identify opportunities for language enhancements and architectural refinements

Protein Alignment Systolic Array Throughput Optimization

Protein comparison is gaining importance year after year since it has been demonstrated that biologists can find cor- relation between different species, or genetic mutations that can lead to cancer and genetic diseases. Protein sequence alignment is the most computational intensive task when performing protein comparison. In order to speed-up alignment, dedicated processors that can perform different computations in parallel have been designed. Among them, the best performance have been achieved using Systolic Arrays. However, when the Processing Elements of the Systolic Array have an internal loop, performance could be highly reduced. In this work we present an architectural strategy to address this problem applying pipeline interleaving; this strategy is applied to a Systolic Array for Smith Waterman algorithm that we designed. Results encourage the adoption of pipeline interleaving for parallel circuits with loop based Processing Elements. We demonstrate that important benefits in terms of higher operating frequency can be derived without so relevant costs as increased complexity, area and power required

Cryptarray A Scalable And Reconfigurable Architecture For Cryptographic Applications

Cryptography is increasingly viewed as a critical technology to fulfill the requirements of security and authentication for information exchange between Internet applications. However, software implementations of cryptographic applications are unable to support the quality of service from a bandwidth perspective required by most Internet applications. As a result, various hardware implementations, from Application-Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), to programmable processors, were proposed to improve this inadequate quality of service. Although these implementations provide performances that are considered better than those produced by software implementations, they still fall short of addressing the bandwidth requirements of most cryptographic applications in the context of the Internet for two major reasons: (i) The majority of these architectures sacrifice flexibility for performance in order to reach the performance level needed for cryptographic applications. This lack of flexibility can be detrimental considering that cryptographic standards and algorithms are still evolving. (ii) These architectures do not consider the consequences of technology scaling in general, and particularly interconnect related problems. As a result, this thesis proposes an architecture that attempts to address the requirements of cryptographic applications by overcoming the obstacles described in (i) and (ii). To this end, we propose a new reconfigurable, two-dimensional, scalable architecture, called CRYPTARRAY, in which bus-based communication is replaced by distributed shared memory communication. At the physical level, the length of the wires will be kept to a minimum. CRYPTARRAY is organized as a chessboard in which the dark and light squares represent Processing Elements (PE) and memory blocks respectively. The granularity and resource composition of the PEs is specifically designed to support the computing operations encountered in cryptographic algorithms in general, and symmetric algorithms in particular. Communication can occur only between neighboring PEs through locally shared memory blocks. Because of the chessboard layout, the architecture can be reconfigured to allow computation to proceed as a pipelined wave in any direction. This organization offers a high computational density in terms of datapath resources and a large number of distributed storage resources that easily support a high degree of parallelism and pipelining. Experimental prototyping a small array on FPGA chips shows that this architecture can run at 80.9 MHz producing 26,968,716 outputs every second in static reconfiguration mode and 20,226,537 outputs every second in dynamic reconfiguration mode

REAL-TIME ADAPTIVE PULSE COMPRESSION ON RECONFIGURABLE, SYSTEM-ON-CHIP (SOC) PLATFORMS

New radar applications need to perform complex algorithms and process a large quantity of data to generate useful information for the users. This situation has motivated the search for better processing solutions that include low-power high-performance processors, efficient algorithms, and high-speed interfaces. In this work, hardware implementation of adaptive pulse compression algorithms for real-time transceiver optimization is presented, and is based on a System-on-Chip architecture for reconfigurable hardware devices. This study also evaluates the performance of dedicated coprocessors as hardware accelerator units to speed up and improve the computation of computing-intensive tasks such matrix multiplication and matrix inversion, which are essential units to solve the covariance matrix. The tradeoffs between latency and hardware utilization are also presented. Moreover, the system architecture takes advantage of the embedded processor, which is interconnected with the logic resources through high-performance buses, to perform floating-point operations, control the processing blocks, and communicate with an external PC through a customized software interface. The overall system functionality is demonstrated and tested for real-time operations using a Ku-band testbed together with a low-cost channel emulator for different types of waveforms

A study on hardware design for high performance artificial neural network by using FPGA and NoC

制度:新 ; 報告番号:甲3421号 ; 学位の種類:博士(工学) ; 授与年月日:2011/9/15 ; 早大学位記番号:新574

Parallelization of dynamic programming recurrences in computational biology

The rapid growth of biosequence databases over the last decade has led to a performance bottleneck in the applications analyzing them. In particular, over the last five years DNA sequencing capacity of next-generation sequencers has been doubling every six months as costs have plummeted. The data produced by these sequencers is overwhelming traditional compute systems. We believe that in the future compute performance, not sequencing, will become the bottleneck in advancing genome science. In this work, we investigate novel computing platforms to accelerate dynamic programming algorithms, which are popular in bioinformatics workloads. We study algorithm-specific hardware architectures that exploit fine-grained parallelism in dynamic programming kernels using field-programmable gate arrays: FPGAs). We advocate a high-level synthesis approach, using the recurrence equation abstraction to represent dynamic programming and polyhedral analysis to exploit parallelism. We suggest a novel technique within the polyhedral model to optimize for throughput by pipelining independent computations on an array. This design technique improves on the state of the art, which builds latency-optimal arrays. We also suggest a method to dynamically switch between a family of designs using FPGA reconfiguration to achieve a significant performance boost. We have used polyhedral methods to parallelize the Nussinov RNA folding algorithm to build a family of accelerators that can trade resources for parallelism and are between 15-130x faster than a modern dual core CPU implementation. A Zuker RNA folding accelerator we built on a single workstation with four Xilinx Virtex 4 FPGAs outperforms 198 3 GHz Intel Core 2 Duo processors. Furthermore, our design running on a single FPGA is an order of magnitude faster than competing implementations on similar-generation FPGAs and graphics processors. Our work is a step toward the goal of automated synthesis of hardware accelerators for dynamic programming algorithms

A Dynamically Reconfigurable Parallel Processing Framework with Application to High-Performance Video Processing

Digital video processing demands have and will continue to grow at unprecedented rates. Growth comes from ever increasing volume of data, demand for higher resolution, higher frame rates, and the need for high capacity communications. Moreover, economic realities force continued reductions in size, weight and power requirements. The ever-changing needs and complexities associated with effective video processing systems leads to the consideration of dynamically reconfigurable systems. The goal of this dissertation research was to develop and demonstrate the viability of integrated parallel processing system that effectively and efficiently apply pre-optimized hardware cores for processing video streamed data. Digital video is decomposed into packets which are then distributed over a group of parallel video processing cores. Real time processing requires an effective task scheduler that distributes video packets efficiently to any of the reconfigurable distributed processing nodes across the framework, with the nodes running on FPGA reconfigurable logic in an inherently Virtual\u27 mode. The developed framework, coupled with the use of hardware techniques for dynamic processing optimization achieves an optimal cost/power/performance realization for video processing applications. The system is evaluated by testing processor utilization relative to I/O bandwidth and algorithm latency using a separable 2-D FIR filtering system, and a dynamic pixel processor. For these applications, the system can achieve performance of hundreds of 640x480 video frames per second across an eight lane Gen I PCIe bus. Overall, optimal performance is achieved in the sense that video data is processed at the maximum possible rate that can be streamed through the processing cores. This performance, coupled with inherent ability to dynamically add new algorithms to the described dynamically reconfigurable distributed processing framework, creates new opportunities for realizable and economic hardware virtualization.\u2

NeuralMatrix: Compute the Entire Neural Networks with Linear Matrix Operations for Efficient Inference

The inherent diversity of computation types within individual deep neural network (DNN) models necessitates a corresponding variety of computation units within hardware processors, leading to a significant constraint on computation efficiency during neural network execution. In this study, we introduce NeuralMatrix, a framework that transforms the computation of entire DNNs into linear matrix operations, effectively enabling their execution with one general-purpose matrix multiplication (GEMM) accelerator. By surmounting the constraints posed by the diverse computation types required by individual network models, this approach provides both generality, allowing a wide range of DNN models to be executed using a single GEMM accelerator and application-specific acceleration levels without extra special function units, which are validated through main stream DNNs and their variant models.Comment: 12 pages, 4figures, Submitted to 11th International Conference on Learning Representation